Predictability of Tropical Vegetation Greenness Using Sea Surface Temperatures
This study employed fine spatial resolution remotely-sensed Enhanced Vegetation Index (EVI) and SST indices from tropical ocean basins to investigate the predictability of tropical vegetation greenness in response to SSTs and established empirical models with optimal parameters for hindcast predictions. Our findings reveal that the pan-tropical EVI was tightly connected to the SSTs over tropical ocean basins, and the strongest impacts of SSTs on EVI were identified mainly over the arid or semi-arid tropical regions.
Our work provides a basis for the prediction of changes in the greenness of tropical terrestrial ecosystems at seasonal to intra-seasonal scales. Moreover, the statistics-based observational relationships have the potential to facilitate the benchmarking of Earth System Models regarding their ability to capture the responses of tropical vegetation growth to long-term signals of oceanic forcings.
Three tropical regions, namely northeastern Brazil, eastern tropical Africa, and northern Australia, that are located in arid or semi-arid climate zones and covered mainly by sparse vegetation including open shrub, were found to be associated with evident influences of SSTs on vegetation growth and consequently high ecological predictability on seasonal to intra-seasonal time scales. Over the tropical rainforests, however, the weakest oceanic influences and thus lowest predictability were identified. The developed statistical models partially predicted the EVI dynamics based on selected SST indices over the pan tropics with limitations owing to the impacts from factors other than climate and model simplicity. As a future direction, more sophisticated statistical models will be tested. We will also evaluate the reliability of the statistics-based model for extracting key oceanic impacts on tropical terrestrial ecosystem production using dynamic experiments with an Earth system model, for example, those from the High Resolution Model Intercomparison Project driven by observed SSTs. We will include vegetation data that is more physiologically related to plant photosynthesis, including the observation-based GPP and SIF. Furthermore, dimension reduction techniques will be applied to quantify the leading modes of SSTs and precipitation and their relationships with vegetation greenness, thus to reduce dimensionality.